Combining SAX and piecewise linear approximation to improve similarity search on financial time series

Abstract

Efficient and accurate similarity searching on a large time series data set is an important but non-trivial problem. In this work, we propose a new approach to improve the quality of similarity search on time series data by combining Symbolic Aggregate Approximation (SAX) and Piecewise Linear Approximation. The approach consists of three steps: transforming real valued time series sequences to symbolic strings via SAX, pattern matching on the symbolic strings and a post-processing via Piecewise Linear Approximation. © 2007 IEEE.